DEVELOPMENT... OpenML
Run
161212

Run 161212

Task 3896 (Supervised Classification) ada_agnostic Uploaded 15-02-2015 by Felicia West
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Flow

weka.LogitBoost_DecisionStump(2)J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
weka.LogitBoost_DecisionStump(2)_E1
weka.LogitBoost_DecisionStump(2)_H1.0
weka.LogitBoost_DecisionStump(2)_I160
weka.LogitBoost_DecisionStump(2)_L-1.7976931348623157E308
weka.LogitBoost_DecisionStump(2)_O1
weka.LogitBoost_DecisionStump(2)_P100
weka.LogitBoost_DecisionStump(2)_S1
weka.LogitBoost_DecisionStump(2)_Wweka.classifiers.trees.DecisionStump
weka.LogitBoost_DecisionStump(2)_Z3.0

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

model
Model readable

A human-readable description of the model that was built.

model
Model serialized

A serialized description of the model that can be read by the tool that generated it.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

22 Evaluation measures

Area under ROC curve
0.9036
Per class
F measure
0.8477
Per class
Kappa
0.5801
KB relative information score
2013.8253
Mean absolute error
0.1997
Mean prior absolute error
0.3732
Number of instances
4562
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.847
Per class
Predictive accuracy
0.8529
Prior entropy
0.8085
Recall
0.8529
Per class
Relative absolute error
0.535
Root mean prior squared error
0.4319
Root mean squared error
0.3209
Root relative squared error
0.743
SciMark benchmark
941.7954
UserCPU time millis
8080
UserCPU time millis training
8080